A Sentinel-2 unsupervised forest mask for European sites

Forests cover one third of Europe's land and significantly contribute to the regional economy. Moreover, they play an essential role in climate regulation. Traditional inventory-based forest data update is often much lower than required. Remote Sensing is a valuable source for forest monitoring, as it provides periodic data on vegetation status. In this context, EU Horizon-2020 MySustainableForest project (MSF, Grant Agreement no 776045) aims at developing remote sensingderived geo-information services for integrated forest management through a web service platform. An unsupervised method to obtain a forest mask over European forests using optical Sentinel-2 data was implemented. K-means algorithm was used for segmenting the images in clusters, which were subsequently assigned to a forest class depending on its overlap with the forest classes of ancillary land cover data. The resulting classification was refined applying a filter and a vegetation mask. The algorithm was tested over 16 sites representing Europe's main biogeographic regions. A confusion matrix was built using points selected via photointerpretation. Validation metrics were computed from the confusion matrix. The results showed that it is possible to develop an automatic forest mask for Europe, (overall accuracy above 90%). Accuracies varied depending on forest characteristics. Best results were achieved in Boreal and Continental forests. Although the algorithm was tuned to consider the diversity of European forests, there is scope for improving the adaptability of MSF Forest Mask, mainly in the southern Mediterranean region, where the mixed effect of tree-grass formations hindered a better forest discrimination. These results may be of interest to forest and land managers and climate modellers.